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CASA 2006 CASA 2006 A Skinning Approach for Dynamic Mesh Compression Khaled Mamou Titus Zaharia Françoise Prêteux
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3D animation industry Context & Objectives Applications Virtual and augmented reality Cartoons Video games and CGI films
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Dynamic 3D content Context & Objectives How to exchange, transmit and visualize such 3D content in a platform-independent manner ?! Content creation Motion capture Skinning models Physical-based simulation …
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Context & Objectives Principle Represent the animation sequence as a set of key-meshes Key-frames Animation Interpolation 3D animation industry: key-frame representations Dynamic 3D content Apply interpolation procedures to generate the in-between frames at the desired framerate
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Context & Objectives Constant topology Time-varying geometry Sequence of meshes with: Constant topology Time-varying geometry Dynamic 3D content Key-frame representations: dynamic 3D meshes Advantages Generality Interoperability Content protection Drawbacks Huge amount of data Need of compact representations
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Objectives Context & Objectives Compression efficiency Compactness of the coded representation Progressive transmission Bitstream adaptation to different, fixed or mobile communication networks and terminal devices Scalable rendering Bitstream adaptation for real-time rendering
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Outline Experimental evaluation Conclusion et perspectives Previous work Skinning-based compression
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Outline Experimental evaluation Conclusion et perspectives Previous work Skinning-based compression
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Wavelets Vertex prediction State of the art MPEG-4/AFX-IC Dynapack AWC GV PCA-based LPCA CPCA PCA Clustering Dynamic 3D mesh compression RT D3DMC Emerging field of research Four families of approaches
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Wavelets Vertex prediction State of the art MPEG-4/AFX-IC Dynapack A more elaborated motion model: skinning AWC GV PCA-based LPCA CPCA PCA Clustering Dynamic 3D mesh compression Skinning-based compression RT D3DMC New motion-based segmentation procedure Temporal DCT-based compression of the residual errors Principle: extension of the RT technique
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Outline Experimental evaluation Conclusion et perspectives Previous work Skinning-based compression
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General view Skinning-basedcompression Temporal DCT Affine motion and weights estimation Motion-based segmentation Static encoder Quantization and arithmetic encoding Prediction residuals Affine transforms Animation weights Partition (Mi)(Mi) M0M0 Compressed DCT coefficients Compressed M 0
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Motion-based segmentation Skinning-basedcompression Objective Partition the mesh vertices into clusters whose motion can be accurately described by a single affine motion
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Motion-based segmentation Skinning-basedcompression Principle For each vertex v, select a neighborhood v*
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Vector of homogeneous coordinates of vertex p at frame i Motion-based segmentation Skinning-basedcompression For each frame i, compute an affine transform A i v … Frame 0Frame 1Frame (F-1) Principle For each vertex v, select a neighborhood v* Store the ( A i v ) i of each vertex as a single vector α v
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Motion-based segmentation Skinning-basedcompression For each frame i, compute an affine transform A i v Principle For each vertex v, select a neighborhood v* Store the ( A i v ) i of each vertex as a single vector α v Cow Determine the partition π = ( π k ) k by applying the k-means clustering algorithm to the set ( α v ) v
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Motion-based segmentation Skinning-basedcompression Dancer For each frame i, compute an affine transform A i v Principle For each vertex v, select a neighborhood v* Store the ( A i v ) i of each vertex as a single vector α v Determine the partition π = ( π k ) k by applying the k-means clustering algorithm to the set ( α v ) v
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General view Skinning-basedcompression Temporal DCT Affine motion and weights estimation Motion-based segmentation Static encoder Quantization and arithmetic encoding Prediction residuals Affine transforms Animation weights Partition (Mi)(Mi) M0M0 Compressed DCT coefficients Compressed M 0
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Affine motion estimation Skinning-basedcompression Principle Model the motion of each cluster k at each frame i by an affine transform H i k Predict the geometry of frame i from frame 0 by using the affine transforms ( H i k ) k
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Affine motion estimation Skinning-basedcompression Performances Captures well the object motion Frame 0Frame 36Predicted frame 36 Error distribution 0% 4% Induces discontinuities at the level of clusters boundaries We need a more elaborated motion model
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Skinning model Skinning-basedcompression Objective Derive a continuous motion field Principle Linearly combine the affine motion of adjacent clusters with appropriate weighting coefficients Compute the animation weights by solving a least squares minimization problem
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General view Skinning-basedcompression Temporal DCT Affine motion and weights estimation Motion-based segmentation Static encoder Quantization and arithmetic encoding Prediction residuals Affine transforms Animation weights Partition (Mi)(Mi) M0M0 Compressed DCT coefficients Compressed M 0
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DCT-based compression of the residual errors Skinning-basedcompression Objective Compress the residual errors by exploiting the temporal correlations Prediction error at frame i and vertex v Principle For each vertex v, compute the spectra of its x, y and z errors Concatenate the spectral coefficients of all vertices into a single vector S Quantize and arithmetically encode S Well-adapted to progressive transmission
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Outline Experimental evaluation Conclusion et perspectives Previous work Skinning-based compression
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Evaluation corpus: Snake Experimentalresults 9179 vertices 134 frames
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Evaluation corpus: Dancer Experimentalresults 7061 vertices 201 frames
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Evaluation corpus: Humanoid Experimentalresults 7646 vertices 154 frames
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Evaluation corpus: Chicken Experimentalresults 3030 vertices 400 frames
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Objective evaluation: criteria Compression rates: bits per frame per vertex (bpfv) Distortion measures: RMSE [MESH tool, Aspert et al, 2002] D : length of the diagonal of the object’s bounding boxExperimentalresults
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Compression results: Chicken Experimentalresults Performances D3DMC & skinning: best performances Skinning: up to 47% gain over D3DMC in term of bitrates RMSE bpfv
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Compression results: Snake Experimentalresults Performances PCA: worst performances (F>>V not verified) Skinning: up to 45% gain over RT in term of bitrates RMSE bpfv
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Compression results: Humanoid Experimentalresults Performances AFX-IC: poor performances: elementary predictor Skinning: up to 67% gain over D3DMC in term of bitrates RMSE bpfv
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Compression results: Dancer Experimentalresults RMSE bpfv Performances GV: re-meshing related problems Skinning: up to 65% gain over GV in term of bitrates
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Outline Experimental evaluation Conclusion et perspectives Previous work Skinning-based compression
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Conclusion & perspectives Summary A new skinning-based compression techniques for dynamic meshes Specifically efficient for articulated dynamic meshes Gains range from 47% to 67% in terms of bitrates over state-of-the-art encoders
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Future work Conclusion & perspectives Optimize the motion-based segmentation stage: How to determine automatically the number of clusters? Multiple and dynamic skinning models: Temporal segmentation of the sequence Compression of other attributes: normals, texture coordinates…
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